ArticleFrontiers in psychology2026
A multidimensional measure of pre-service teachers' perceived technological competence for teaching: integrating TPACK, DigCompEdu, and self-efficacy.
Article in Frontiers in psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Technological competence for teaching is multidimensional, encompassing digital tool use, professional digital practice, assessment, pedagogical integration, adaptability, and technology-related self-efficacy. Existing measures often assess these components separately rather than within a unified instrument. Objective: This study developed and validated a multidimensional self-report scale of pre-service teachers' technological competence for teaching by integrating complementary elements of the Technological Pedagogical Content Knowledge (TPACK) framework, the European Framework for the Digital Competence of Educators (DigCompEdu), and technology-related self-efficacy. Methods: A multi-phase scale-development and validation design involved two independent cohorts of pre-service teachers at a public university in Türkiye. The pilot sample ( Results: EFA supported a six-factor, 33-item structure: Foundational Digital Tool Use; Professional Digital Communication and Collaboration; Assessment, Feedback, and Learning Analytics; Pedagogically Aligned Technology Integration; Instructional Technology Troubleshooting and Adaptability; and Teaching-Related Technology Self-Efficacy. The factors explained 62.8% of the variance. CFA showed excellent fit, Conclusion: Findings provide initial psychometric support for a six-dimensional self-report measure of technological competence for teaching. The instrument is best interpreted at the subscale level, and its scores represent self-reported technological competence rather than direct observation of teaching performance. It is appropriate for research, formative use, and program evaluation. Further validation across institutions, longitudinal designs, and independent performance-based measures is recommended.
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